WesLemon/SWENG7

1import gradio as gr2 3from vega_datasets import data4 5cars = data.cars()6iris = data.iris()7 8def metrics(dataset):9    if dataset == "iris":10        return gr.ScatterPlot.update(11            value=iris,12            x="petalWidth",13            y="petalLength",14            color="species",15            title="Iris Dataset",16            color_legend_title="Species",17            x_title="Petal Width",18            y_title="Petal Length",19            tooltip=["petalWidth", "petalLength", "species"],20            caption="",21        )22    else:23        return [gr.ScatterPlot.update(24            value=cars,25            x="Horsepower",26            y="Miles_per_Gallon",27            color="Origin",28            tooltip="Name",29            title="ROC Curve",30            y_title="False Positive Rate",31            x_title="True Positive Rate",32            color_legend_title="Origin of Car",33            caption="ROC Curve of Model",34        ), gr.DataFrame.update(35            value=[["Predicted Positive",1,0],["Predicted Negative",0,1]], 36            max_rows=3,37            max_cols=3,38            label="Confusion Matrix",39            show_label=True)]40 41 42with gr.Blocks() as plots:43    with gr.Row():44        with gr.Column():45            dataset = gr.Dropdown(choices=["ROC", "iris"], value="ROC")46    with gr.Row():47        with gr.Column():48            plot = gr.ScatterPlot(show_label=False).style(container=True)49        with gr.Column():50            matrix = gr.Dataframe(headers=["", "Actually Positive", "Actually Negative"],show_label=False).style(container=True)51    dataset.change(metrics, inputs=dataset, outputs=plot)52    plots.load(fn=metrics, inputs=dataset, outputs=[plot,matrix])53 54if __name__ == "__main__":55    plots.launch()